This lab activity is meant to give an overview of a materials science machine learning workflow. It covers sections starting from initial data cleaning and ends with analyzing various predictions the model could make.

Each section includes a few exercises which highlights key points from each step.

The notebook can be ran without edits to the code, but there a points throughout where edits can be made to investigate different aspects of the model that could be changed.

Attached as supplementary info is a set of slides and associated material for use as a one week module for introducing students to machine learning in materials science.

For a quick video walkthrough of the lab material please see the youtube playlist here: https://youtu.be/nNQToVpz3_o

Researchers should cite this work as follows:

• BENJAMIN AFFLERBACH, Rundong Jiang, Josh Tappan, DANE MORGAN (2022), "Machine Learning Lab Module," https://nanohub.org/resources/intromllab. (DOI: 10.21981/CPNK-XE48).

  BibTex | EndNote

1. machine learning
2. materials science
3. NCN Group - Materials Science